Wanted to do data science in a higher education context. LEO differs from conventional datasets for education and reveals many unique things about universities and the courses offered thereof, partly because it is an aggregation of tax data and student loan records with large(r) sample sizes. There also isn't any visualizaton tool for this dataset available despite its significance.
Also shout out to Dannie Rhodes and Adam who were also part of the team.
What it does
Generates a data visualization of the LEO dataset based on the user inputs (university name, course type, or a combination of the two).
How we built it
Challenges we ran into
Learning the technologies. 1/4 of us knew the technologies well enough, so a significant amount of time was spent learning the ropes of how to program in these languages/frameworks.
We tried to surreptitiously parse the csv client-side in order to avoid creating an entire back-end just for this purpose but this proved to be highly difficult and slow.
Accomplishments that we're proud of
Building something that compiles and works in less than 24 hours.
What we learned
Jamie: How to deploy a REST API in Django. Jordan: ReactJS and Python
What's next for LEO Data Science
Improving the backend.